ETF: extended tensor factorization model for personalizing prediction of review helpfulness

Online reviews are valuable sources of information for a variety of decision-making processes such as purchasing products. As the number of online reviews is growing rapidly, it becomes increasingly difficult for users to identify those that are helpful. This has motivated research into the problem of identifying high quality and helpful reviews automatically. The current methods assume that the helpfulness of a review is independent from the readers of that review. However, we argue that the quality of a review may not be the same for different users. For example, a professional and an amateur photographer may rate the helpfulness of a review very differently. In this paper, we introduce the problem of predicting a personalized review quality for recommendation of helpful reviews. To address this problem, we propose a series of increasingly sophisticated probabilistic graphical models, based on Matrix Factorization and Tensor Factorization. We evaluate the proposed models using a database of 1.5 million reviews and more than 13 million quality ratings obtained from Epinions.com. The experiments demonstrate that the proposed latent factor models outperform the state-of-the art approaches using textual and social features. Finally, our experiments confirm that the helpfulness of a review is indeed not the same for all users and that there are some latent factors that affect a user's evaluation of the review quality.